IDEAS home Printed from https://ideas.repec.org/r/eee/finana/v63y2019icp1-9.html
   My bibliography  Save this item

Economic constraints and stock return predictability: A new approach

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Ma, Feng & Wang, Ruoxin & Lu, Xinjie & Wahab, M.I.M., 2021. "A comprehensive look at stock return predictability by oil prices using economic constraint approaches," International Review of Financial Analysis, Elsevier, vol. 78(C).
  2. Xu, Yongan & Wang, Jianqiong & Chen, Zhonglu & Liang, Chao, 2021. "Economic policy uncertainty and stock market returns: New evidence," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
  3. Xu, Yongan & Liang, Chao & Wang, Jianqiong, 2023. "Financial stress and returns predictability: Fresh evidence from China," Pacific-Basin Finance Journal, Elsevier, vol. 78(C).
  4. Wen, Zhuzhu & Gong, Xu & Ma, Diandian & Xu, Yahua, 2021. "Intraday momentum and return predictability: Evidence from the crude oil market," Economic Modelling, Elsevier, vol. 95(C), pages 374-384.
  5. Liang, Chao & Xu, Yongan & Wang, Jianqiong & Yang, Mo, 2022. "Whether dimensionality reduction techniques can improve the ability of sentiment proxies to predict stock market returns," International Review of Financial Analysis, Elsevier, vol. 82(C).
  6. Zhang, Wei & Wang, Pengfei & Li, Yi, 2020. "Intraday momentum in Chinese commodity futures markets," Research in International Business and Finance, Elsevier, vol. 54(C).
  7. Yaojie Zhang & Feng Ma & Chao Liang & Yi Zhang, 2021. "Good variance, bad variance, and stock return predictability," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(3), pages 4410-4423, July.
  8. Nonejad, Nima, 2020. "Crude oil price volatility and equity return predictability: A comparative out-of-sample study," International Review of Financial Analysis, Elsevier, vol. 71(C).
  9. Bai, Fan & Zhang, Yaqi & Chen, Zhonglu & Li, Yan, 2023. "The volatility of daily tug-of-war intensity and stock market returns," Finance Research Letters, Elsevier, vol. 55(PA).
  10. Zhang, Yaojie & Ma, Feng & Wei, Yu, 2019. "Out-of-sample prediction of the oil futures market volatility: A comparison of new and traditional combination approaches," Energy Economics, Elsevier, vol. 81(C), pages 1109-1120.
  11. Kartikay Gupta & Niladri Chatterjee, 2021. "Stocks Recommendation from Large Datasets Using Important Company and Economic Indicators," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 28(4), pages 667-689, December.
  12. Jurdi, Doureige J., 2022. "Predicting the Australian equity risk premium," Pacific-Basin Finance Journal, Elsevier, vol. 71(C).
  13. Dai, Zhifeng & Zhu, Huan, 2021. "Indicator selection and stock return predictability," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).
  14. Yongan Xu & Jianqiong Wang & Zhonglu Chen & Chao Liang, 2023. "Sentiment indices and stock returns: Evidence from China," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(1), pages 1063-1080, January.
  15. Dai, Zhifeng & Dong, Xiaodi & Kang, Jie & Hong, Lianying, 2020. "Forecasting stock market returns: New technical indicators and two-step economic constraint method," The North American Journal of Economics and Finance, Elsevier, vol. 53(C).
  16. Dai, Zhifeng & Zhu, Huan, 2020. "Stock return predictability from a mixed model perspective," Pacific-Basin Finance Journal, Elsevier, vol. 60(C).
  17. Chen, Wang & Chevallier, Julien & Wang, Jiqian & Zhong, Juandan, 2022. "Stock market return predictability revisited: Evidence from a new index constructing the oil market," Finance Research Letters, Elsevier, vol. 49(C).
  18. Lan Bai & Xiafei Li & Yu Wei & Guiwu Wei, 2022. "Does crude oil futures price really help to predict spot oil price? New evidence from density forecasting," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(3), pages 3694-3712, July.
  19. Li, Yan & Huo, Jiale & Xu, Yongan & Liang, Chao, 2023. "Belief-based momentum indicator and stock market return predictability," Research in International Business and Finance, Elsevier, vol. 64(C).
  20. Zhifeng Dai & Huiting Zhou, 2020. "Prediction of Stock Returns: Sum-of-the-Parts Method and Economic Constraint Method," Sustainability, MDPI, vol. 12(2), pages 1-13, January.
  21. Jin, Daxiang & He, Mengxi & Xing, Lu & Zhang, Yaojie, 2022. "Forecasting China's crude oil futures volatility: How to dig out the information of other energy futures volatilities?," Resources Policy, Elsevier, vol. 78(C).
  22. Zhifeng Dai & Jie Kang & Hua Yin, 2023. "Forecasting equity risk premium: A new method based on wavelet de‐noising," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(4), pages 4331-4352, October.
  23. Zhang, Zhikai & He, Mengxi & Zhang, Yaojie & Wang, Yudong, 2021. "Realized skewness and the short-term predictability for aggregate stock market volatility," Economic Modelling, Elsevier, vol. 103(C).
  24. Dai, Zhifeng & Zhou, Huiting & Kang, Jie & Wen, Fenghua, 2021. "The skewness of oil price returns and equity premium predictability," Energy Economics, Elsevier, vol. 94(C).
  25. Danyan Wen & Mengxi He & Yaojie Zhang & Yudong Wang, 2022. "Forecasting realized volatility of Chinese stock market: A simple but efficient truncated approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(2), pages 230-251, March.
  26. Nonejad, Nima, 2022. "Equity premium prediction using the price of crude oil: Uncovering the nonlinear predictive impact," Energy Economics, Elsevier, vol. 115(C).
  27. Dai, Zhifeng & Kang, Jie & Wen, Fenghua, 2021. "Predicting stock returns: A risk measurement perspective," International Review of Financial Analysis, Elsevier, vol. 74(C).
  28. Dai, Zhifeng & Zhu, Huan & Kang, Jie, 2021. "New technical indicators and stock returns predictability," International Review of Economics & Finance, Elsevier, vol. 71(C), pages 127-142.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.